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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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-depth literature study of edge systems, distributed systems and simulation. You will perform experimental studies of computer systems with the emphasis on the time and energy consumption predictions. From
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-depth literature study of edge systems, distributed systems and simulation. You will perform experimental studies of computer systems with the emphasis on the time and energy consumption predictions. From
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placed on: The research project’s scientific merit, research-related relevance and innovation The applicant’s estimated academic and personal ability to carry out the project within the allotted time frame
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machine learning and/or image analysis/computer vision A solid and documented background in machine learning, mathematics, linear algebra, and/or statistics Documented knowledge and experience in Python
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on your estimated date for the doctoral dissertation, or confirmation that your PhD thesis has been submitted. Documentation of a completed doctoral degree must be presented before taking up the position
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image